To address high data traffic demands of sixth-generation (6G) networks, this paper proposes a novel architecture that integrates autonomous aerial vehicles (AAVs) and multi-functional reconfigurable intelligent surfaces (MF-RISs) as AM-RIS in fluid antenna (FA)-assisted full-duplex (FD) networks. The AM-RIS provides hybrid functionalities, including signal reflection, amplification, and energy harvesting (EH), potentially improving both signal coverage and sustainability. Meanwhile, FA facilitates fine-grained spatial adaptability at FD-enabled base station (BS), which complements residual self-interference (SI) suppression. We aim at maximizing the overall energy efficiency (EE) by jointly optimizing transmit DL beamforming at BS, UL user power, configuration of AM-RIS, and positions of the FA and AM-RIS. Owing to the hybrid continuous-discrete parameters and high dimensionality of the intractable problem, we have conceived a self-optimized multi-agent hybrid deep reinforcement learning (DRL) framework (SOHRL), which integrates multi-agent deep Q-networks (DQN) and multi-agent proximal policy optimization (PPO), respectively handling discrete and continuous actions. To enhance self-adaptability, an attention-driven state representation and meta-level hyperparameter optimization are incorporated, enabling multi-agents to autonomously adjust learning hyperparameters. Simulation results validate the effectiveness of the proposed AM-RIS-enabled FA-aided FD networks empowered by SOHRL algorithm. The results reveal that SOHRL outperforms benchmarks of the case without attention mechanism and conventional hybrid/multi-agent/standalone DRL. Moreover, AM-RIS in FD achieves the highest EE compared to half-duplex, conventional rigid antenna arrays, partial EH, and conventional RIS without amplification, highlighting its potential as a compelling solution for EE-aware wireless networks.

翻译：为应对第六代（6G）网络的高数据流量需求，本文提出一种新型架构，将自主空中飞行器（AAVs）与多功能可重构智能表面（MF-RISs）集成为空中多功能RIS（AM-RIS），并部署于流体天线（FA）辅助的全双工（FD）网络中。AM-RIS提供信号反射、放大与能量采集（EH）等混合功能，有望同时提升信号覆盖与可持续性。同时，FA在支持全双工的基站（BS）处实现细粒度空间适应性，以补充残余自干扰（SI）抑制能力。我们旨在通过联合优化BS的下行发射波束成形、上行用户功率、AM-RIS配置以及FA与AM-RIS的位置，最大化整体能量效率（EE）。针对该难解问题中混合连续-离散参数与高维度特性，我们提出一种自优化多智能体混合深度强化学习（DRL）框架（SOHRL），该框架整合了多智能体深度Q网络（DQN）与多智能体近端策略优化（PPO），分别处理离散与连续动作。为增强自适应能力，引入注意力驱动的状态表示与元级超参数优化，使多智能体能够自主调整学习超参数。仿真结果验证了所提出的SOHRL算法赋能AM-RIS辅助FA全双工网络的有效性。结果表明，SOHRL在无注意力机制方案及传统混合/多智能体/单智能体DRL基准方法中表现更优。此外，相较于半双工、传统刚性天线阵列、部分能量采集及无放大功能的常规RIS，全双工中的AM-RIS实现了最高EE，凸显其作为EE感知型无线网络解决方案的潜力。